Laurens Arp presents his work titled “Dynamic macro-scale traffic flow optimisation using crowd-sourced urban movement data” at IEEEMDM 2020

Traffic congestion tends to be bad for the environment, the economy, and above all: the drivers’ moods. As such, it is a worthwhile cause to pursue improvements for; in particular, being computer- and data scientists, using data-driven methods to try to alleviate this problem seemed a particularly exciting approach. This is just what we (Laurens Arp, Dyon van Vreumingen, Daniela Gahwens and Mitra Baratchi) published a paper for in the IEEE MDM 2020 proceedings. The project, which started as a course project for Mitra’s Urban Computing course at Leiden University, evolved first into a short paper submission to the Netmob Future Cities Challenge (FCC), and subsequently into a full paper submission to the MDM conference.

The main idea of the method proposed was to redistribute traffic by imposing external costs (or rewards) to specific road segments. We used a movement dataset for Tokyo, provided by Foursquare for the FCC, from which we could derive which proportion of drivers would want to go to and from specific parts of the city. By combining this with a macro-scale traffic flow model (Greenshields), we were able to compute the occupancy of specific roads and the total travel time this resulted in. This model could then be used as an objective function for black-box optimisation; we would optimise the cost parameters of road segments so that the optimal number of desired routes got redirected to alternative roads such that the overall traffic time was minimised.

The amount of improvement (under the Greenshields model) we were able to achieve was highly dependent on the number of cars we estimated would be on the road at the same time. The best improvement achieved was 13.15% for a little over 13 500 cars (925 hours), and the worst was 1.35% for just under 113 000 cars (437 hours). Interestingly, even the relatively modest improvements, occurring for large amounts of cars, could still be meaningful, because there are more drivers to benefit from them. We also added a few fairness analyses to the paper, the results of which did not seem to indicate any unfair disadvantage to individual drivers. You can find the pre-recorded conference presentation here.

We hope that our paper will be able to contribute to the perpetually on-going efforts worldwide of causing less damage to the environment, boosting the economy, and perhaps also helping some drivers’ moods.

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